0.1 Prep.

alldata <- readr::read_csv(here::here("data/ldn_stop_search_with_lnglat_36m.csv"), show_col_types = FALSE)
offence <- readr::read_csv(here::here("data/ldn_stop_search_offence_36m.csv"), show_col_types = FALSE)

The map of London city

# make sure you have the same direcory stucture to get London wards shapefile
london_wards_sf <- read_sf(here::here("data/London-wards-2018/London-wards-2018_ESRI/London_Ward.shp"))
# st_geometry(london_wards_sf) # what is the geometry ?

# london_wards_sf = projected CRS:  OSGB 1936 / British National Grid

# change the CRS to use WGS84 lng/lat pairs
london_wgs84 <-  london_wards_sf %>%
  st_transform(4326) # transform CRS to WGS84, latitude/longitude

st_geometry(london_wgs84) # what is the geometry ?
## Geometry set for 657 features 
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -0.51 ymin: 51.3 xmax: 0.334 ymax: 51.7
## Geodetic CRS:  WGS 84
## First 5 geometries:
ggplot() + 
  geom_sf(data = london_wgs84, fill = "#3B454A", size = 0.125, colour = "#b2b2b277")

get_sf <- function(data) {
  sf_all <- st_as_sf(data, 
                coords=c('lng', 'lat'),
                crs = 4326)
  # filter points using the map
  sf_within <- st_filter(sf_all, london_wgs84)
  return(sf_within)
}

0.2 Plot. 1

Stop-and-search cases surged in mid 2020 over the past 36 months. Most objects of search cases are on “controlled drugs”.

alldata %>% 
  mutate(controlled_drugs = ifelse(object_of_search == "Controlled drugs", "Controlled drugs", "Other")) %>% 
  mutate(controlled_drugs = ifelse(is.na(controlled_drugs), "Other", controlled_drugs)) %>% 
  group_by(year, month, controlled_drugs) %>% 
  summarize(n_case = n()) %>% 
  mutate(month_year = as.Date(paste(year, "-", month, "-01", sep=""))) %>% 
  # label for the graph
  mutate(label = if_else(month_year == as.Date("2022-09-01"), as.character(controlled_drugs), NA_character_)) %>% 
  filter(month_year >= "2019-10-01") %>% 
  ggplot(aes(x = month_year, y = n_case, group = controlled_drugs, color = controlled_drugs)) + 
  geom_vline(
    xintercept = c(as.Date("2019-10-01"), as.Date("2020-01-01"), as.Date("2020-04-01"), as.Date("2020-07-01"), 
                   as.Date("2020-10-01"), as.Date("2021-01-01"), as.Date("2021-04-01"), as.Date("2021-07-01"), 
                   as.Date("2021-10-01"), as.Date("2022-01-01"), as.Date("2022-04-01"), as.Date("2022-07-01"),
                   as.Date("2022-09-01")), color = "grey80", size = .4) + 
  geom_segment(
    data = tibble(y = seq(0, 45000, by = 2500), x1 = as.Date("2019-10-01"), x2 = as.Date("2022-09-01")),
    aes(x = x1, xend = x2, y = y, yend = y),
    inherit.aes = FALSE,
    color = "grey80",
    size = .4
  ) +
  geom_segment(
    data = tibble(y = 0, x1 = as.Date("2019-10-01"), x2 = as.Date("2022-09-01")),
    aes(x = x1, xend = x2, y = y, yend = y),
    inherit.aes = FALSE,
    color = "grey40",
    size = .4
  ) +
  geom_line() + 
  labs(
    x = NULL, 
    y = NULL, 
    title = "Stop-and-search cases surged in mid 2020 over the past 36 months", 
    caption = "Source: UK Metropolitan Police"
  ) +
  scale_x_date(limits = c(as.Date("2019-10-01"), as.Date("2023-08-01")), 
               date_labels = "%y-%b", 
               breaks = c(as.Date("2019-11-01"), as.Date("2020-05-01"), 
                          as.Date("2020-11-01"), as.Date("2021-05-01"), 
                          as.Date("2021-11-01"), as.Date("2022-05-01")))+
  geom_text_repel(
    aes(color = controlled_drugs, label = label),
    xlim = c(as.Date("2022-10-01"), NA),
    family = "Lato",
    fontface = "bold",
    size = 4, 
    segment.size = .7,
    segment.alpha = .5,
    segment.linetype = "dotted"
  ) +
  theme(legend.position = "none", 
        plot.title.position = "plot", 
        plot.caption=element_text(hjust = 0.65))

## Plot. 2

Over 80% of “Controlled drugs,”Offensive weapons”, “Stolen goods”.

Grouping the records by objects of search, we can see that each group’s points cluster at the center of the city of London.

alldata %>% 
  group_by(object_of_search) %>% 
  summarize(cnt = n()) %>% 
  mutate(perc = cnt / sum(cnt)) %>% 
  slice_max(perc, n = 5)
## # A tibble: 5 × 3
##   object_of_search            cnt   perc
##   <chr>                     <int>  <dbl>
## 1 Controlled drugs         851687 0.592 
## 2 Offensive weapons        189071 0.131 
## 3 Stolen goods             122620 0.0853
## 4 <NA>                     110137 0.0766
## 5 Article for use in theft  60161 0.0418
ggplot() + 
  geom_sf(data = london_wgs84, fill = "#3B454A", size = 0.125, colour = "#b2b2b277") +
  # add points from dtop and search shapefile
  geom_sf(
    data = get_sf(offence %>% filter(year == 2020 & month == 5)), 
    aes(fill = object_of_search), 
    color = "white", size = 1.5, alpha = 0.7, shape = 21, 
    show.legend = FALSE
  ) + 
  hrbrthemes::theme_ft_rc(grid = "", strip_text_face = "bold") +
  theme(axis.text = element_blank()) + 
  theme(strip.text = element_text(color = "white")) + 
  theme_minimal() +
  coord_sf(datum = NA) + # remove coord
  facet_wrap(~object_of_search) +
  labs(title = "Locations of Stop&Search in London, 2020 May") +
  NULL

ggplot() + 
  geom_sf(data = london_wgs84, fill = "#3B454A", size = 0.125, colour = "#b2b2b277") +
  # add points from dtop and search shapefile
  geom_sf(
    data = get_sf(offence %>% filter(year == 2020 & month == 6)), 
    aes(fill = object_of_search), 
    color = "white", size = 1.5, alpha = 0.7, shape = 21, 
    show.legend = FALSE
  ) + 
  hrbrthemes::theme_ft_rc(grid = "", strip_text_face = "bold") +
  theme(axis.text = element_blank()) + 
  theme(strip.text = element_text(color = "white")) + 
  theme_minimal() +
  coord_sf(datum = NA) + # remove coord
  facet_wrap(~object_of_search) +
  labs(title = "Locations of Stop&Search in London, 2020 June") +
  NULL

0.3 Plot. 3

The West End had most of the stop-and-search cases in Jun, 2020. St. James’s is the second most “stop-and-searched” district.

get_distri <- function(data, year, month) {
  temp <- london_wgs84 %>%
  mutate(count = lengths(
    st_contains(london_wgs84, 
                get_sf(data %>% filter(year == year & 
                                     month == month)))))
  return(temp)
}

tmap::tmap_mode("view") # interactive map
tmap::tm_shape(get_distri(offence, 2020, 6)) +
  tm_polygons("count")